MTC: AI may not be your co‑counsel—and a recent SDNY decision just made that painfully clear. ⚖️🤖

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SDNY Heppner Ruling: Public AI Use Breaks Attorney-Client PrivilegE!

In United States v. Heppner, Judge Jed Rakoff of the Southern District of New York ruled that documents a criminal defendant generated with a publicly accessible AI tool and later sent to his lawyers were not protected by either attorney‑client privilege or the work‑product doctrine. That decision should be a wake‑up call for every lawyer who has ever dropped client facts into a public chatbot.

The court’s analysis followed traditional privilege principles rather than futuristic AI theory. Privilege requires confidential communication between a client and a lawyer made for the purpose of obtaining legal advice. In Heppner, the AI tool was “obviously not an attorney,” and there was no “trusting human relationship” with a licensed professional who owed duties of loyalty and confidentiality. Moreover, the platform’s privacy policy disclosed that user inputs and outputs could be collected and shared with third parties, undermining any reasonable expectation of confidentiality. In short, the defendant’s AI‑generated drafts looked less like protected client notes and more like research entrusted to a third‑party service.

For sometime now, I’ve warned on The Tech‑Savvy Lawyer.Page has warned practitioners not to paste client PII or case‑specific facts into generative AI tools, particularly public models whose terms of use and training practices erode confidentiality. We have consistently framed AI as an extension of a lawyer’s existing ethical duties, not a shortcut around them. I have encouraged readers to treat these systems like any other non‑lawyer vendor that must be vetted, contractually constrained, and configured before use. That perspective aligns squarely with Heppner’s outcome: once you treat a public AI as a casual brainstorming partner, you risk treating your client’s confidences as discoverable data.

A Tech-Savvy Lawyer Avoids AI Privilege Waiver With Confidentiality Safeguards!

For lawyers, this has immediate implications under the ABA Model Rules. Model Rule 1.1 on competence now explicitly includes understanding the “benefits and risks associated” with relevant technology, and recent ABA guidance on generative AI emphasizes that uncritical reliance on these tools can breach the duty of competence. A lawyer who casually uses public AI tools with client facts—without reading the terms of use, configuring privacy, or warning the client—may fail the competence test in both technology and privilege preservation. The Tech‑Savvy Lawyer.Page repeatedly underscores this point, translating dense ethics opinions into practical checklists and workflows so that even lawyers with only moderate tech literacy can implement safer practices.

Model Rule 1.6 on confidentiality is equally implicated. If a lawyer discloses client confidential information to a public AI platform that uses data for training or reserves broad rights to disclose to third parties, that disclosure can be treated like sharing with any non‑necessary third party, risking waiver of privilege. Ethical guidance stresses that lawyers must understand whether an AI provider logs, trains on, or shares client data and must adopt reasonable safeguards before using such tools. That means reading privacy policies, toggling enterprise settings, and, in many cases, avoiding consumer tools altogether for client‑specific prompts.

Does a private, paid AI make a difference? Possibly, but only if it is structured like other trusted legal technology. Enterprise or legal‑industry tools that contractually commit not to train on user data and to maintain strict confidentiality can better support privilege claims, because confidentiality and reasonable expectations are preserved. Tools like Lexis‑style or Westlaw‑style AI offerings, deployed under robust business associate and security agreements, look more like traditional research platforms or litigation support vendors within Model Rules 5.1 and 5.3, which govern supervisory duties over non‑lawyer assistants. The Tech‑Savvy Lawyer.Page has emphasized this distinction, encouraging lawyers to favor vetted, enterprise‑grade solutions over consumer chatbots when client information is involved.

Enterprise AI Vetting Checklist for Lawyers: Contracts, NDA, No Training

The tech‑savvy lawyer in 2026 is not the one who uses the most AI; it is the one who knows when not to use it. Before entering client facts into any generative AI, lawyers should ask: Is this tool configured to protect client confidentiality? Have I satisfied my duties of competence and communication by explaining the risks to my client (Model Rules 1.1 and 1.4)? And if a court reads this platform’s privacy policy the way Judge Rakoff did, will I be able to defend my privilege claims with a straight face to a court or to a disciplinary bar?

AI may be a powerful drafting partner, but it is not your co‑counsel and not your client’s confidant. The tech‑savvy lawyer—of the sort championed by The Tech‑Savvy Lawyer.Page—treats it as a tool: carefully vetted, contractually constrained, and ethically supervised, or not used at all. 🔒🤖

MTC: Lawyers and AI Oversight: What the VA’s Patient Safety Warning Teaches About Ethical Law Firm Technology Use! ⚖️🤖

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Human-in-the-loop is the point: Effective oversight happens where AI meets care—aligning clinical judgment, privacy, and compliance with real-world workflows.

The Department of Veterans Affairs’ experience with generative AI is not a distant government problem; it is a mirror held up to every law firm experimenting with AI tools for drafting, research, and client communication. I recently listened to an interview by Terry Gerton of the Federal News Network of Charyl Mason, Inspector General of the Department of Veterans Affairs, “VA rolled out new AI tools quickly, but without a system to catch mistakes, patient safety is on the line” and gained some insights on how lawyers can learn from this perhaps hastilly impliment AI program. VA clinicians are using AI chatbots to document visits and support clinical decisions, yet a federal watchdog has warned that there is no formal mechanism to identify, track, or resolve AI‑related risks—a “potential patient safety risk” created by speed without governance. In law, that same pattern translates into “potential client safety and justice risk,” because the core failure is identical: deploying powerful systems without a structured way to catch and correct their mistakes.

The oversight gap at the VA is striking. There is no standardized process for reporting AI‑related concerns, no feedback loop to detect patterns, and no clearly assigned responsibility for coordinating safety responses across the organization. Clinicians may have helpful tools, but the institution lacks the governance architecture that turns “helpful” into “reliably safe.” When law firms license AI research platforms, enable generative tools in email and document systems, or encourage staff to “try out” chatbots on live matters without written policies, risk registers, or escalation paths, they recreate that same governance vacuum. If no one measures hallucinations, data leakage, or embedded bias in outputs, risk management has given way to wishful thinking.

Existing ethics rules already tell us why that is unacceptable. Under ABA Model Rule 1.1, competence now includes understanding the capabilities and limitations of AI tools used in practice, or associating with someone who does. Model Rule 1.6 requires lawyers to critically evaluate what client information is fed into self‑learning systems and whether informed consent is required, particularly when providers reuse inputs for training. Model Rules 5.1, 5.2, and 5.3 extend these obligations across partners, supervising lawyers, and non‑lawyer staff: if a supervised lawyer or paraprofessional relies on AI in a way that undermines client protection, firm leadership cannot plausibly claim ignorance. And rules on candor to tribunals make clear that “the AI drafted it” is never a defense to filing inaccurate or fictitious authority.

Explaining the algorithm to decision-makers: Oversight means making AI risks understandable to judges, boards, and the public—clearly and credibly.

What the VA story adds is a vivid reminder that effective AI oversight is a system, not a slogan. The inspector general emphasized that AI can be “a helpful tool” only if it is paired with meaningful human engagement: defined review processes, clear routes for reporting concerns, and institutional learning from near misses. For law practice, that points directly toward structured workflows. AI‑assisted drafts should be treated as hypotheses, not answers. Reasonable human oversight includes verifying citations, checking quotations against original sources, stress‑testing legal conclusions, and documenting that review—especially in high‑stakes matters involving liberty, benefits, regulatory exposure, or professional discipline.

For lawyers with limited to moderate tech skills, this should not be discouraging; done correctly, AI governance actually makes technology more approachable. You do not need to understand model weights or training architectures to ask practical questions: What data does this tool see? When has it been wrong in the past? Who is responsible for catching those errors before they reach a client, a court, or an opposing party? Thoughtful prompts, standardized checklists for reviewing AI output, and clear sign‑off requirements are all well within reach of every practitioner.

The VA’s experience also highlights the importance of mapping AI uses and classifying their risk. In health care, certain AI use cases are obviously safety‑critical; in law, the parallel category includes anything that could affect a person’s freedom, immigration status, financial security, public benefits, or professional license. Those use cases merit heightened safeguards: tighter access control, narrower scoping of AI tasks, periodic sampling of outputs for quality, and specific training for the lawyers who use them. Importantly, this is not a “big‑law only” discipline. Solo and small‑firm lawyers can implement proportionate governance with simple written policies, matter‑level notes showing how AI was used, and explicit conversations with clients where appropriate.

Critically, AI does not dilute core professional responsibility. If a generative system inserts fictitious cases into a brief or subtly mischaracterizes a statute, the duty of candor and competence still rests squarely on the attorney who signs the work product. The VA continues to hold clinicians responsible for patient care decisions, even when AI is used as a support tool; the law should be no different. That reality should inform how lawyers describe AI use in engagement letters, how they supervise junior lawyers and staff, and how they respond when AI‑related concerns arise. In some situations, meeting ethical duties may require forthright client communication, corrective filings, and revisions to internal policies.

AI oversight starts at the desk: Lawyers must be able to interrogate model outputs, data quality, and risk signals—before technology impacts patient care.

The practical lesson from the VA’s AI warning is straightforward. The “human touch” in legal technology is not a nostalgic ideal; it is the safety mechanism that makes AI ethically usable at all. Lawyers who embrace AI while investing in governance—policies, training, and oversight calibrated to risk—will be best positioned to align with the ABA’s evolving guidance, satisfy courts and regulators, and preserve hard‑earned client trust. Those who treat AI as a magic upgrade and skip the hard work of oversight are, knowingly or not, accepting that their clients may become the test cases that reveal where the system fails. In a profession grounded in judgment, the real innovation is not adopting AI; it is designing a practice where human judgment still has the final word.

MTC

MTC: Everyday Tech, Extraordinary Evidence—Again: How Courts Are Punishing Fake Digital and AI Data ⚖️📱

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Check your Ai work - AI fraud can meet courtroom consequences.

In last month’s editorial, “Everyday Tech, Extraordinary Evidence,” we walked through how smartphones, dash cams, and wearables turned the Minnesota ICE shooting into a case study in modern evidence practice, from rapid preservation orders to multi‑angle video timelines.📱⚖️ We focused on the positive side: how deliberate intake, early preservation, and basic synchronization tools can turn ordinary devices into case‑winning proof.📹 This follow‑up tackles the other half of the equation—what happens when “evidence” itself is fake, AI‑generated, or simply unverified slop, and how courts are starting to respond with serious sanctions.⚠️

From Everyday Tech to Everyday Scrutiny

The original article urged you to treat phones and wearables as critical evidentiary tools, not afterthoughts: ask about devices at intake, cross‑reference GPS trails, and treat cars as rolling 360‑degree cameras.🚗⌚ We also highlighted the Minnesota Pretti shooting as an example of how rapid, court‑ordered preservation of video and other digital artifacts can stop crucial evidence from “disappearing” before the facts are fully understood.📹 Those core recommendations still stand—if anything, they are more urgent now that generative AI makes it easier to fabricate convincing “evidence” that never happened.🤖

The same tools that helped you build robust, data‑driven reconstructions—synchronized bystander clips, GPS logs, wearables showing movement or inactivity—are now under heightened scrutiny for authenticity.📊 Judges and opposing counsel are no longer satisfied with “the video speaks for itself”; they want to know who created it, how it was stored, whether metadata shows AI editing, and what steps counsel took to verify that the file is what it purports to be.📁

When “Evidence” Is Fake: Sanctions Arrive

We have moved past the hypothetical stage. Courts are now issuing sanctions—sometimes terminating sanctions—when parties present fake or AI‑generated “evidence” or unverified AI research.💥

These are not “techie” footnotes; they are vivid warnings that falsified or unverified digital and AI data can end careers and destroy cases.🚨

ABA Model Rules: The Safety Rails You Ignore at Your Peril

Train to verify—defend truth in the age of AI.

Your original everyday‑tech playbook already fits neatly within ABA Model Rule 1.1 and Comment 8’s duty of technological competence; the new sanctions landscape simply clarifies the stakes.📚

  • Rule 1.1 (Competence): You must understand the benefits and risks of relevant technology, which now clearly includes generative AI and deepfake tools.⚖️ Using AI to draft or “enhance” without checking the output is not a harmless shortcut—it is a competence problem.

  • Rule 1.6 (Confidentiality): Uploading client videos, wearable logs, or sensitive communications to consumer‑grade AI sites can expose them to unknown retention and training practices, risking confidentiality violations.🔐

  • Rule 3.3 (Candor to the Tribunal) and Rule 4.1 (Truthfulness): Presenting AI‑altered video or fake citations as if they were genuine is the very definition of misrepresentation, as the New York and California sanction orders make clear.⚠️ Even negligent failure to verify can be treated harshly once the court’s patience for AI excuses runs out.

  • Rules 5.1–5.3 (Supervision): Supervising lawyers must ensure that associates, law clerks, and vendors understand that AI outputs are starting points, not trustworthy final products, and that fake or manipulated digital evidence will not be tolerated.👥

Bridging Last Month’s Playbook With Today’s AI‑Risk Reality

In Last month’s editorial, we urged three practical habits: ask about devices, move fast on preservation, and build a vendor bench for extraction and authentication.📱⌚🚗 This month, the job is to wrap those habits in explicit AI‑risk controls that lawyers with modest tech skills can realistically follow.🧠

  1. Never treat AI as a silent co‑counsel. If you use AI to draft research, generate timelines, or “enhance” video, you must independently verify every factual assertion and citation, just as you would double‑check a new associate’s memo.📑 “The AI did it” is not a defense; courts have already said so.

  2. Preserve the original, disclose the enhancement. Our earlier advice to keep raw smartphone files and dash‑cam footage now needs one more step: if you use any enhancement (AI or otherwise), label it clearly and be prepared to explain what was done, why, and how you ensured that the content did not change.📹

  3. Use vendors and examiners as authenticity firewalls. Just as we suggested, bringing in digital forensics vendors to extract phone and wearable data, you should now consider them for authenticity challenges as well—especially where the opposing side may have incentives or tools to create deepfakes.🔍 A simple expert declaration that a file shows signs of AI manipulation can be the difference between a credibility battle and a terminating sanction.

  4. Train your team using real sanction orders. Nothing clarifies the risk like reading Judge Castel’s order in the ChatGPT‑citation case or Judge Kolakowski’s deepfake ruling in Mendones.⚖️ Incorporate those cases into short internal trainings and CLEs; they translate abstract “AI ethics” into concrete, courtroom‑tested consequences.

  5. Document your verification steps. For everyday tech evidence, a simple log—what files you received, how you checked metadata, whether you compared against other sources, which AI tools (if any) you used, and what you did to confirm their outputs—can demonstrate good faith if a judge later questions your process.📋

Final Thoughts: Authenticity as a First‑Class Question

be the rock star! know how to use ai responsibly in your work!

In the first editorial, the core message was that everyday devices are quietly turning into your best witnesses.📱⌚ The new baseline is that every such “witness” will be examined for signs of AI contamination, and you will be expected to have an answer when the court asks, “What did you do to make sure this is real?”🔎

Lawyers with limited to moderate tech skills do not need to reverse‑engineer neural networks or master forensic software. Instead, they must combine the practical habits from January’s piece—asking, preserving, synchronizing—with a disciplined refusal to outsource judgment to AI.⚖️ In an era of deepfakes and hallucinated case law, authenticity is no longer a niche evidentiary issue; it is the moral center of digital advocacy.✨

Handled wisely, your everyday tech strategy can still deliver “extraordinary evidence.” Handled carelessly, it can just as quickly produce extraordinary sanctions.🚨

MTC

Word of the Week: "Constitutional AI" for Lawyers - What It Is, Why It Matters for ABA Rules, and How Solo & Small Firms Should Use It!

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Constitutional AI’s ‘helpful, harmless, honest’ standard is a solid starting point for lawyers evaluating AI platforms.

The term “Constitutional AI” appeared this week in a Tech Savvy Lawyer post about the MTC/PornHub breach as a cybersecurity wake‑up call for lawyers 🚨. That article used it to highlight how AI systems (like those law firms now rely on) must be built and governed by clear, ethical rules — much like a constitution — to protect client data and uphold professional duties. This week’s Word of the Week unpacks what Constitutional AI really means and explains why it matters deeply for solo, small, and mid‑size law firms.

🔍 What is Constitutional AI?

Constitutional AI is a method for training large language models so they follow a written set of high‑level principles, called a “constitution” 📜. Those principles are designed to make the AI helpful, honest, and harmless in its responses.

As Claude AI from Anthropic explains:
“Constitutional AI refers to a set of techniques developed by researchers at Anthropic to align AI systems like myself with human values and make us helpful, harmless, and honest. The key ideas behind Constitutional AI are aligning an AI’s behavior with a ‘constitution’ defined by human principles, using techniques like self‑supervision and adversarial training, developing constrained optimization techniques, and designing training data and model architecture to encode beneficial behaviors.” — Claude AI, Anthropic (July 7th, 2023).

In practice, Constitutional AI uses the model itself to critique and revise its own outputs against that constitution. For example, the model might be told: “Do not generate illegal, dangerous, or unethical content,” “Be honest about what you don’t know,” and “Protect user privacy.” It then evaluates its own answers against those rules before giving a final response.

Think of it like a junior associate who’s been given a firm’s internal ethics manual and told: “Before you send that memo, check it against these rules.” Constitutional AI does that same kind of self‑checking, but at machine speed.

🤝 How Constitutional AI Relates to Lawyers

For lawyers, Constitutional AI is important because it directly shapes how AI tools behave when handling legal work 📚. Many legal AI tools are built on models that use Constitutional AI techniques, so understanding this concept helps lawyers:

  • Judge whether an AI assistant is likely to hallucinate, leak sensitive info, or give ethically problematic advice.

  • Choose tools whose underlying AI is designed to be more transparent, less biased, and more aligned with professional norms.

  • Better supervise AI use in the firm, which is a core ethical duty under the ABA Model Rules.

Solo and small firms, in particular, often rely on off‑the‑shelf AI tools (like chatbots or document assistants). Knowing that a tool is built on Constitutional AI principles can give more confidence that it’s designed to avoid harmful outputs and respect confidentiality.

⚖️ Why It Matters for ABA Model Rules

For solo and small firms, asking whether an AI platform aligns with Constitutional AI’s standards is a practical first step in choosing a trustworthy tool.

The ABA’s Formal Opinion 512 on generative AI makes clear that lawyers remain responsible for all work done with AI, even if an AI tool helped draft it 📝. Constitutional AI is relevant here because it’s one way that AI developers try to build in ethical guardrails that align with lawyers' obligations.

Key connections to the Model Rules:

  • Rule 1.1 (Competence): Lawyers must understand the benefits and risks of the technology they use. Knowing that a tool uses Constitutional AI helps assess whether it’s reasonably reliable for tasks like research, drafting, or summarizing.

  • Rule 1.6 (Confidentiality): Constitutional AI models are designed to refuse to disclose sensitive information and to avoid memorizing or leaking private data. This supports the lawyer’s duty to make “reasonable efforts” to protect client confidences.

  • Rule 5.1 / 5.3 (Supervision): Managing partners and supervising attorneys must ensure that AI tools used by staff are consistent with ethical rules. A tool built on Constitutional AI principles is more likely to support, rather than undermine, those supervisory duties.

  • Rule 3.3 (Candor to the Tribunal): Constitutional AI models are trained to admit uncertainty and avoid fabricating facts or cases, which helps reduce the risk of submitting false or misleading information to a court.

In short, Constitutional AI doesn’t relieve lawyers of their ethical duties, but it can make AI tools safer and more trustworthy when used under proper supervision.

🛡️ The “Helpful, Harmless, and Honest” Principle

The three pillars of Constitutional AI — helpful, harmless, and honest — are especially relevant for lawyers:

  • Helpful: The AI should provide useful, relevant information that advances the client’s matter, without unnecessary or irrelevant content.

  • Harmless: The AI should avoid generating illegal, dangerous, or unethical content, and should respect privacy and confidentiality.

  • Honest: The AI should admit when it doesn’t know something, avoid fabricating facts or cases, and not misrepresent its capabilities.

For law firms, this “helpful, harmless, and honest” standard is a useful mental checklist when using AI:

  • Is this AI output actually helpful to the client’s case?

  • Could this output harm the client (e.g., by leaking confidential info or suggesting an unethical strategy)?

  • Is the AI being honest (e.g., not hallucinating case law or pretending to know facts it can’t know)?

If the answer to any of those questions is “no,” the AI output should not be used without significant human review and correction.

🛠️ Practical Takeaways for Law Firms

For solo, small, and mid‑size firms, here’s how to put this into practice:

Lawyers need to screen AI tools and ensure they are aligned with ABA Model Rules.

  1. Know your tools. When evaluating a legal AI product, ask whether it’s built on a Constitutional AI–style model (e.g., Claude). That tells you it’s designed with explicit ethical constraints.

  2. Treat AI as a supervised assistant. Never let AI make final decisions or file work without a lawyer’s review. Constitutional AI reduces risk, but it doesn’t eliminate the need for human judgment.

  3. Train your team. Make sure everyone in the firm understands that AI outputs must be checked for accuracy, confidentiality, and ethical compliance — especially when using third‑party tools.

  4. Update your engagement letters and policies. Disclose to clients when AI is used in their matters, and explain how the firm supervises it. This supports transparency under Rule 1.4 and Rule 1.6.

  5. Focus on “helpful, honest, harmless.” Use Constitutional AI as a mental checklist: Is this AI being helpful to the client? Is it honest about its limits? Is it harmless (no bias, no privacy leaks)? If not, don’t rely on it.

MTC: The 2026 Hardware Hike: Why Law Firms Must Budget for the "AI Squeeze" Now!

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Lawyers need to be ready for $prices$ in tech to go up next year due to increased AI use!

A perfect storm is brewing in the hardware market. It will hit law firm budgets harder than expected in 2026. Reports from December 2025 confirm that major manufacturers like Dell, Lenovo, and HP are preparing to raise PC and laptop prices by 15% to 20% early next year. The catalyst is a global shortage of DRAM (Dynamic Random Access Memory). This shortage is driven by the insatiable appetite of AI servers.

While recent headlines note that giants like Apple and Samsung have the supply chain power to weather this surge, the average law firm does not. This creates a critical strategic challenge for managing partners and legal administrators.

The timing is unfortunate. Legal professionals are adopting AI tools at a record pace. Tools for eDiscovery, contract analysis, and generative drafting require significant computing power to run smoothly. In 2024, a laptop with 16GB of RAM was standard. Today, running local privacy-focused AI models or heavy eDiscovery platforms makes 32GB the new baseline. 64GB is becoming the standard for power users.

“Don’t just meet today’s AI demands—exceed them. Upgrade to 32GB or 64GB of RAM now, not later. AI adoption in legal practice is accelerating exponentially. The memory you think is “enough” today will be the bottleneck tomorrow. Firms that overspec their hardware now will avoid costly mid-cycle replacements and gain a competitive edge in speed and efficiency.”
— 💡 PRO TIP: Future-Proof Your Firm's Hardware Now

We face a paradox. We need more memory to remain competitive, but that memory is becoming scarce and expensive. The "AI Squeeze" is real. Chipmakers are prioritizing high-profit memory for data center AI over the standard memory used in law firm laptops. This supply shift drives up the bill of materials for every new workstation (low end when you compare them “high-profit memory data centers) you plan to buy.

Update your firm’s tech budget for 2026 by prioritizing ram for your next technology upgrade.

Law firms should act immediately. First, audit your hardware refresh cycles. If you planned to upgrade machines in Q1 or Q2 of 2026, accelerate those purchases to the current quarter. You could save 20% per unit by buying before the price hikes take full effect.

Second, adjust your 2026 technology budget. A flat budget will buy you less power next year. You cannot afford to downgrade specifications. Buying underpowered laptops will frustrate fee earners and throttle the efficiency gains you expect from your AI investments.

Finally, prioritize RAM over storage. Cloud storage is cheap and abundant. Memory is not. When configuring new machines, allocate your budget to 32GB or 64GB (or more) of RAM rather than a larger hard drive.

The hardware market is shifting. The cost of innovation is rising. Smart firms will plan for this reality today rather than paying the premium tomorrow.

🧪🎧 TSL Labs Bonus Podcast: Open vs. Closed AI — The Hidden Liability Trap in Your Firm ⚖️🤖

/ The Tech-Savvy Lawyer.Page/

Welcome to TSL Labs Podcast Experiment. 🧪🎧 In this special "Deep Dive" bonus episode, we strip away the hype surrounding Generative AI to expose a critical operational risk hiding in plain sight: the dangerous confusion between "Open" and "Closed" AI systems.

Featuring an engaging discussion between our Google Notebook AI hosts, this episode unpacks the "Swiss Army Knife vs. Scalpel" analogy that every managing partner needs to understand. We explore why the "Green Light" tools you pay for are fundamentally different from the "Red Light" public models your staff might be using—and why treating them the same could trigger an immediate breach of ABA Model Rule 5.3. From the "hidden crisis" of AI embedded in Microsoft 365 to the non-negotiable duty to supervise, this is the essential briefing for protecting client confidentiality in the age of algorithms.

In our conversation, we cover the following:

  • [00:00] – Introduction: The hidden danger of AI in law firms.

  • [01:00] – The "AI Gap": Why staff confuse efficiency with confidentiality.

  • [02:00] – The Green Light Zone: Defining secure, "Closed" AI systems (The Scalpel).

  • [03:45] – The Red Light Zone: Understanding "Open" Public LLMs (The Swiss Army Knife).

  • [04:45] – "Feeding the Beast": How public queries actively train the model for everyone else.

  • [05:45] – The Duty to Supervise: ABA Model Rules 5.3 and 1.1[8] implications.

  • [07:00] – The Hidden Crisis: AI embedded in ubiquitous tools (Microsoft 365, Adobe, Zoom).

  • [09:00] – The Training Gap: Why digital natives assume all prompt boxes are safe.

  • [10:00] – Actionable Solutions: Auditing tools and the "Elevator vs. Private Room" analogy.

  • [12:00] – Hallucinations: Vendor liability vs. Professional negligence.

  • [14:00] – Conclusion: The final provocative thought on accidental breaches.

RESOURCES

Mentioned in the episode

Software & Cloud Services mentioned in the conversation

MTC: The Hidden Danger in Your Firm: Why We Must Teach the Difference Between “Open” and “Closed” AI!

/ The Tech-Savvy Lawyer.Page/

Does your staff understand the difference between “free” and “paid” aI? Your license could depend on it!

I sit on an advisory board for a school that trains paralegals. We meet to discuss curriculum. We talk about the future of legal support. In a recent meeting, a presentation by a private legal research company caught my attention. It stopped me cold. The topic was Artificial Intelligence. The focus was on use and efficiency. But something critical was missing.

The lesson did not distinguish between public-facing and private tools. It treated AI as a monolith. This is a dangerous oversimplification. It is a liability waiting to happen.

We are in a new era of legal technology. It is exciting. It is also perilous. The peril comes from confusion. Specifically, the confusion between paid, closed-system legal research tools and public-facing generative AI.

Your paralegals, law clerks, and staff use these tools. They use them to draft emails. They use them to summarize depositions. Do they know where that data goes? Do you?

The Two Worlds of AI

There are two distinct worlds of AI in our profession.

First, there is the world of "Closed" AI. These are the tools we pay for - i.e., Lexis+/Protege, Westlaw Precision, Co-Counsel, Harvey, vLex Vincent, etc. These platforms are built for lawyers. They are walled gardens. You pay a premium for them. (Always check the terms and conditions of your providers.) That premium buys you more than just access. It buys you privacy. It buys you security. When you upload a case file to Westlaw, it stays there. The AI analyzes it. It does not learn from it for the public. It does not share your client’s secrets with the world. The data remains yours. The confidentiality is baked in.

Then, there is the world of "Open" or "Public" AI. This is ChatGPT. This is Perplexity. This is Claude. These tools are miraculous. But they are also voracious learners.

When you type a query into the free version of ChatGPT, you are not just asking a question. You are training the model. You are feeding the beast. If a paralegal types, "Draft a motion to dismiss for John Doe, who is accused of embezzlement at [Specific Company]," that information leaves your firm. It enters a public dataset. It is no longer confidential.

This is the distinction that was missing from the lesson plan. It is the distinction that could cost you your license.

The Duty to Supervise

Do you and your staff know when you can and can’t use free AI in your legal work?

You might be thinking, "I don't use ChatGPT for client work, so I'm safe." You are wrong.

You are not the only one doing the work. Your staff is doing the work. Your paralegals are doing the work.

Under the ABA Model Rules of Professional Conduct, you are responsible for them. Look at Rule 5.3. It covers "Responsibilities Regarding Nonlawyer Assistance." It is unambiguous. You must make reasonable efforts to ensure your staff's conduct is compatible with your professional obligations.

If your paralegal breaches confidentiality using AI, it is your breach. If your associate hallucinates a case citation using a public LLM, it is your hallucination.

This connects directly to Rule 1.1, Comment 8. This represents the duty of technology competence. You cannot supervise what you do not understand. You must understand the risks associated with relevant technology. Today, that means understanding how Large Language Models (LLMs) handle data.

The "Hidden AI" Problem

I have discussed this on The Tech-Savvy Lawyer.Page Podcast. We call it the "Hidden AI" crisis. AI is creeping into tools we use every day. It is in Adobe. It is in Zoom. It is in Microsoft 365.

Public-facing AI is useful. I use it. I love it for marketing. I use it for brainstorming generic topics. I use it to clean up non-confidential text. But I never trust it with a client's name. I never trust it with a very specific fact pattern.

A paid legal research tool is different. It is a scalpel. It is precise. It is sterile. A public chatbot is a Swiss Army knife found on the sidewalk. It might work. But you don't know where it's been.

The Training Gap

The advisory board meeting revealed a gap. Schools are teaching students how to use AI. They are teaching prompts. They are teaching speed. They are not emphasizing the where.

The "where" matters. Where does the data go?

We must close this gap in our own firms. You cannot assume your staff knows the difference. To a digital native, a text box is a text box. They see a prompt window in Westlaw. They see a prompt window in ChatGPT. They look the same. They act the same.

They are not the same.

One protects you. The other exposes you.

A Practical Solution

I have written about this in my blog posts regarding AI ethics. The solution is not to ban AI. That is impossible. It is also foolish. AI is a competitive advantage.

* Always check the terms of use in your agreements with private platforms to determine if your client confidential data and PII are protected.

The solution is policies and training.

  1. Audit Your Tools. Know what you have. Do you have an enterprise license for ChatGPT? If so, your data might be private. If not, assume it is public.

  2. Train on the "Why." Don't just say "No." Explain the mechanism. Explain that public AI learns from inputs. Use the analogy of a confidential conversation in a crowded elevator versus a private conference room.

  3. Define "Open" vs. "Closed." Create a visual guide. List your "Green Light" tools (Westlaw, Lexis, etc.). List your "Red Light" tools for client data (Free ChatGPT, personal Gmail, etc.).

  4. Supervise Output. Review the work. AI hallucinates. Even paid tools can make mistakes. Public tools make up cases entirely. We have all seen the headlines. Don't be the next headline.

The Expert Advantage

The line between “free” and “paid” ai could be a matter of keeping your bar license!

On The Tech-Savvy Lawyer.Page, I often say that technology should make us better lawyers, not lazier ones.

Using Lexis+/Protege, Westlaw Precision, Co-Counsel, Harvey, vLex Vincent, etc. is about leveraging a curated, verified database. It is about relying on authority. Using a public LLM for legal research is about rolling the dice.

Your license is hard-earned. Your reputation is priceless. Do not risk them on a free chatbot.

The lesson from the advisory board was clear. The schools are trying to keep up. But the technology moves faster than the curriculum. It is up to us. We are the supervisors. We are the gatekeepers.

Take time this week. Gather your team. Ask them what tools they use. You might be surprised. Then, teach them the difference. Show them the risks.

Be the tech-savvy lawyer your clients deserve. Be the supervisor the Rules require.

The tools are here to stay. Let’s use them effectively. Let’s use them ethically. Let’s use them safely.

MTC

🎙️Ep. 126: AI and Access to Justice With Pearl.com Associate General Counsel Nick Tiger

/ The Tech-Savvy Lawyer.Page/

Our next guest is Nick Tiger, Associate General Counsel at Pearl.com, Nick shares insights on integrating AI into legal practice. Pearl.com champions AI and human expertise for professional services. He outlines practical uses such as market research, content creation, intake automation, and improved billing efficiency, while stressing the need to avoid liability through robust human oversight.

Nick is a legal leader at Pearl.com, partnering on product design, technology, and consumer-protection compliance strategy. He previously served as Head of Product Legal at EarnIn, an earned-wage access pioneer, building practical guidance for responsible feature launches, and as Senior Counsel at Capital One, supporting consumer products and regulatory matters. Nick holds a J.D. from the University of Missouri–Kansas City, lives in Richmond, Virginia, and is especially interested in using technology to expand rural community access to justice.

During the conversation, Nick highlights emerging tools, such as conversation wizards and expert-matching systems, that enhance communication and case preparation. He also explains Pearl AI's unique model, which blends chatbot capabilities with human expert verification to ensure accuracy in high-stakes or subjective matters.

Nick encourages lawyers to adopt human-in-the-loop protocols and consider joining Pearl's expert network to support accessible, reliable legal services.

Join Nick and me as we discuss the following three questions and more!

  1. What are the top three most impactful ways lawyers can immediately implement AI technology in their practices while avoiding the liability pitfalls that have led to sanctions in recent high-profile cases?

  2. Beyond legal research and document review, what are the top three underutilized or emerging AI applications that could transform how lawyers deliver value to clients, and how should firms evaluate which technologies to adopt?

  3. What are the top three criteria Pearl uses to determine when human expert verification is essential versus when AI alone is sufficient? How can lawyers apply this framework to develop their own human-in-the-loop protocols for AI-assisted legal work, and how is Perl different from its competitors?

In our conversation, we cover the following:

[00:56] Nick's Tech Setup

[07:28] Implementing AI in Legal Practices

[17:07] Emerging AI Applications in Legal Services

[26:06] Pearl AI's Unique Approach to AI and Legal Services

[31:42] Developing Human-in-the-Loop Protocols

[34:34] Pearl AI's Advantages Over Competitors

[36:33] Becoming an Expert on Pearl AI

Resources:

Connect with Nick:

Nick's LinkedIn: linkedin.com/in/nicktigerjd

Pearl.com Website: pearl.com

Pearl.com Expert Application Portal: era.justanswer.com/

Pearl.com LinkedIn: linkedin.com/company/pearl-com

Pearl.com X: x.com/Pearldotcom

ABA Resources:

ABA Formal Opinion 512: https://www.americanbar.org/content/dam/aba/administrative/professional_responsibility/ethics-opinions/aba-formal-opinion-512.pdf

Hardware mentioned in the conversation:

Anker Backup Battery / Power Bank: anker.com/collections/power-banks

Software & Cloud Services mentioned in the conversation:

🎙️TSL Labs! MTC: The Hidden AI Crisis in Legal Practice: Why Lawyers Must Unmask Embedded Intelligence Before It's Too Late!

/ The Tech-Savvy Lawyer.Page/

📌 Too Busy to Read This Week's Editorial?

Join us for a professional deep dive into essential tech strategies for AI compliance in your legal practice. 🎙️ This AI-powered discussion unpacks the November 17, 2025, editorial, MTC: The Hidden AI Crisis in Legal Practice: Why Lawyers Must Unmask Embedded Intelligence Before It's Too Late! with actionable intelligence on hidden AI detection, confidentiality protocols, ethics compliance frameworks, and risk mitigation strategies. Artificial intelligence has been silently operating inside your most trusted legal software for years, and under ABA Formal Opinion 512, you bear full responsibility for all AI use, whether you knowingly activated it or it came as a default software update. The conversation makes complex technical concepts accessible to lawyers with varying levels of tech expertise—from tech-hesitant solo practitioners to advanced users—so you'll walk away with immediate, actionable steps to protect your practice, your clients, and your professional reputation.

In Our Conversation, We Cover the Following

00:00:00 - Introduction: Overview of TSL Labs initiative and the AI-generated discussion format

00:01:00 - The Silent Compliance Crisis: How AI has been operating invisibly in your software for years

00:02:00 - Core Conflict: Understanding why helpful tools simultaneously create ethical threats to attorney-client privilege

00:03:00 - Document Creation Vulnerabilities: Microsoft Word Co-pilot and Grammarly's hidden data processing

00:04:00 - Communication Tools Risks: Zoom AI Companion and the cautionary Otter.ai incident

00:05:00 - Research Platform Dangers: Westlaw and Lexis+ AI hallucination rates between 17-33%

00:06:00 - ABA Formal Opinion 512: Full lawyer responsibility for AI use regardless of awareness

00:07:00 - Model Rule 1.6 Analysis: Confidentiality breaches through third-party AI systems

00:08:00 - Model Rule 5.3 Requirements: Supervising AI tools with the same diligence as human assistants

00:09:00 - Five-Step Compliance Framework: Technology audits and vendor agreement evaluation

00:10:00 - Firm Policies and Client Consent: Establishing protocols and securing informed consent

00:11:00 - The Verification Imperative: Lessons from the Mata v. Avianca sanctions case

00:12:00 - Billing Considerations: Navigating hourly versus value-based fee models with AI

00:13:00 - Professional Development: Why tool learning time is non-billable competence maintenance

00:14:00 - Ongoing Compliance: The necessity of quarterly reviews as platforms rapidly evolve

00:15:00 - Closing Remarks: Resources and call to action for tech-savvy innovation

Resources

Mentioned in the Episode

Software & Cloud Services Mentioned in the Conversation

🎙️ Ep. # 124: AI Governance Expert Nikki Mehrpoo Shares the Triple E Protocol for Implementing Responsible AI and Legal Practice While Maintaining Ethical Compliance and Protecting Client Data.

/ The Tech-Savvy Lawyer.Page/

My next guest is Nikki Mehrpoo. She is a nationally recognized leader in AI governance for law practices, known for her practical, ethical, and innovation-focused strategies. Today, she details her Triple-E Protocol and shares key steps for safely leveraging AI in legal work.

Join Nikki Mehrpoo and me as we discuss the following three questions and more!

  1. Based on your pioneering work with “Govern Before You Automate,” what are the top three foundational steps every lawyer should take to implement AI responsibly, and what are the top three mistakes lawyers make with AI?

  2. What are your top three tips or tricks when using AI in your work?

  3. When assessing the next AI platform from a service provider, what are the top three questions lawyers should be asking?

In our conversation, we cover the following:

  • 00:00:00 – Welcome and guest’s background 🌟

  • 00:01:00 – Current tech setup and cloud-based workflows ☁️

  • 00:02:00 – Privacy and IP management, not client confidentiality 🔐

  • 00:03:00 – Document deduplication with Effingo 📄

  • 00:04:00 – Hardware: HP Omni Book 7 Laptop, HP monitors, iPhone 💻📱

  • 00:05:00 – Efficiency tools: Text Expander, personal workflow shortcuts ⌨️

  • 00:06:00 – Balancing technology innovation and risk management ⚖️

  • 00:07:00 – Adapting to change, ongoing legal tech education 🧑‍💻

  • 00:08:00 – Triple-E Framework: Educate, Empower, Elevate 🚀

  • 00:09:00 – Governance, supervision duties, policy setting 🛡️

  • 00:10:00 – Human verification as a standard for all legal AI output 🧑‍⚖️

  • 00:12:00 – Real-world examples: AI hallucinations, bias, and due diligence ⚠️

  • 00:13:00 – IT vs. AI expertise, communicating across teams 🛠️

  • 00:14:00 – Chief AI Governance Officer, governance in legal innovation 🏛️

  • 00:15:00 – Global compliance, EU AI Act, international standards 🌐

  • 00:16:00 – Hidden AI in legacy software, policy gaps 🔎

  • 00:17:00 – Education as continuous legal responsibility 📚

  • 00:18:00 – Better results through prompt engineering 🔤

  • 00:19:00 – Verify, verify, verify: never trust without review ✔️

  • 00:20:00 – ABA Formal Opinion 512: standards for responsible legal AI 📜

  • 00:21:00 – Nikki’s Triple-E Protocol, governance best practices 📊

  • 00:22:00 – Data origin, bias, and auditability in legal AI systems 🧩

  • 00:23:00 – Frameworks for “govern before you automate” in legal workflows 🔒

  • 00:24:00 – Importance of internal hosting and zero retention policies 🏢

  • 00:25:00 – Maintaining confidentiality with third-party AI and HIPAA compliance 🤫

  • 00:26:00 – Where to find Nikki and connect 🌐

Resources

Connect with Nikki Mehrpoo

Mentioned in the episode

Hardware mentioned in the conversation

Software & Cloud Services mentioned in the conversation